Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Riccardo Concu (Author) FCUP; Kleandrova, VV (Author) Other; Speck Planche, A (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: Nanotoxicology

Vol. 11

Pages: 891-906

ISSN: 1743-5390

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 65 Citations

Scopus - 73 Citations

Other information

Authenticus ID: P-00N-8KQ

DOI: 10.1080/17435390.2017.1379567

Abstract (EN): Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs. In this work, we develop a unified QSTR-perturbation model based on artificial neural networks, aimed at simultaneously predicting general toxicity profiles of NPs under diverse experimental conditions. The model is derived from 54,371NP-NP pair cases generated by applying the perturbation theory to a set of 260 unique NPs, and showed an accuracy higher than 97% in both training and validation sets. Physicochemical interpretation of the different descriptors in the model are additionally provided. The QSTR-perturbation model is then employed to predict the toxic effects of several NPs not included in the original dataset. The theoretical results obtained for this independent set are strongly consistent with the experimental evidence found in the literature, suggesting that the present QSTR-perturbation model can be viewed as a promising and reliable computational tool for probing the toxicity of NPs.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16